This method is used to detect missing values for an array-like object. This function takes a scalar or array-like object and indicates whether values are missing (“NaN“ in numeric arrays, “None“ or “NaN“ in object arrays, “NaT“ in datetimelike).
Syntax : pandas.isna(obj)
Argument :
- obj : scalar or array-like, Object to check for null or missing values.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing package import numpy
import pandas
# string "deep" is not nan value print (pandas.isna( "deep" ))
# numpy.nan represents a nan value print (pandas.isna(numpy.nan))
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Output :
False True
Example 2 :
Python3
# importing package import numpy
import pandas
# create and view data array = numpy.array([[ 1 , numpy.nan, 3 ],
[ 4 , 5 , numpy.nan]])
print (array)
# numpy.nan represents a nan value print (pandas.isna(array))
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Output :
[[ 1. nan 3.] [ 4. 5. nan]] [[False True False] [False False True]]
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